All of Ansh R's Comments + Replies
Two hiring (and personally-motivated) questions:
- What would be a good pathway for a software engineer to become a viable member of your technical staff? You can assume that the engineer has had zero or minimal exposure to ML throughout their academic/professional career. If this isn't sufficiently different from what would be recommended to a software engineer interested in alignment in general, feel free to skip this, unless you think there are particular things you'd recommend someone brushing up on before applying to work with you.
- Would you be comfortable sharing the structure of your compensation packages (e.g. mostly salary with possible bonuses, even combination of salary and equity, etc.)?
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3y
Re 1:
It’s probably going to be easier to get good at the infrastructure engineering side of things than the ML side of things, so I’ll assume that that’s what you’re going for.
For our infra engineering role, we want to hire people who are really productive and competent at engineering various web systems quickly. (See the bulleted list of engineering responsibilities on the job page.) There are some people who are qualified for this role without having much professional experience, because they’ve done a lot of Python programming and web programming as hobbyists. Most people who want to become more qualified for this work should seek out a job that’s going to involve practicing these skills. For example, being a generalist backend engineer at a startup, especially if you’re going to be working with ML, is likely to teach you a bunch of the skills that are valuable to us. You’re more likely to learn these skills quickly if you take your job really seriously and try hard to be very good at it--you should try to take on more responsibilities when you get the opportunity to do so, and generally practice the skill of understanding the current technical situation and business needs and coming up with plans to quickly and effectively produce value.
Re 2:
Currently our compensation packages are usually entirely salary. We don’t have equity because we’re a nonprofit. We’re currently unsure how to think about compensation policy--we’d like to be able to offer competitive salaries so that we can hire non-EA talent for appropriate roles (because almost all the talent is non-EA), but there are a bunch of complexities associated with this.
As someone interested in applying for Research Engineering roles in the near future, what would be your criterion for determining whether someone self-taught is “worth-interviewing”? (Also a question for others who are familiar with hiring practices at various AI safety organizations).